Jordan J. Bird

Jordan J. Bird

Posts Tagged

AI

This study into the statistical extraction and machine learning possibilities of EEG brainwave data was published at the 9th International Conference on Intelligent Systems 2018 on Madeira Island, Portugal.

Abstract – This work aims to find discriminative EEG-based features and appropriate classification methods that can categorise brainwave patterns based on their level of activity or frequency for mental state recognition useful for human-machine interaction. By using the Muse headband with four EEG sensors (TP9, AF7, AF8, TP10), we categorised three possible states such as relaxing, neutral and concentrating based on a few states of mind defined by cognitive behavioural studies. We have created a dataset with five individuals and sessions lasting one minute for each class of mental state in order to train and test different methods. Given the proposed set of features extracted from the EEG headband five signals (alpha, beta, theta, delta, gamma), we have tested a combination of different features selection algorithms and classifier models to compare their performance in terms of recognition accuracy and number of features needed. Different tests such as 10-fold cross validation were performed. Results show that only 44 features from a set of over 2100 features are necessary when used with classical classifiers such as Bayesian Networks, Support Vector Machines and Random Forests, attaining an overall accuracy over 87%.

Abstract – Many image classification models have been introduced to help tackle the foremost issue of recognition accuracy. Image classification is one of the core problems in Computer Vision field with a large variety of practical applications. Examples include object recognition for robotic manipulation, pedestrian or obstacle detection for autonomous vehicles, among others. A lot of attention has been associated with Machine Learning, specifically neural networks such as the Convolutional Neural Network (CNN) winning image classification competitions. This work proposes the study and investigation of such a CNN architecture model (i.e. Inception-v3) to establish whether it would work best in terms of accuracy and efficiency with new image datasets via Transfer Learning. The retrained model is evaluated, and the results are compared to some state-of-the-art approaches.

This idea for a chatbot that actively learnt through interaction was developed for a final year project whilst studying for a Bachelor’s Degree in Computer Science from Aston University (Birmingham, UK). The work proved effective and as such a paper was written for UKCI2018 (18th Annual UK Workshop on Computational Intelligence). The work will also be published in Springer’s Advances in Intelligent Systems and Computing.

Abstract – In this paper we propose an approach to a chatbot software that is able to learn from interaction via text messaging between human-bot and bot-bot. The bot listens to a user and decides whether or not it knows how to reply to the message accurately based on current knowledge, otherwise it will set about to learn a meaningful response to the message through pattern matching based on its previous experience. Similar methods are used to detect offensive messages, and are proved to be effective at overcoming the issues that other chatbots have experienced in the open domain. A philosophy of giving preference to too much censorship rather than too little is employed given the failure of Microsoft Tay. In this work, a layered approach is devised to conduct each process, and leave the architecture open to improvement with more advanced methods in the future. Preliminary results show an improvement over time in which the bot learns more responses. A novel approach of message simplification is added to the bot’s architecture, the results suggest that the algorithm has a substantial improvement on the bot’s conversational performance at a factor of three.